Entropy-guided micro-genetic algorithm for multiuser detection in CDMA communications

This paper presents a low-complexity genetic algorithm (μ-GA) for multiuser detection. The probabilities of mutation and crossover of the algorithm are on-line tuned up based on the analysis of the individuals' fitness entropy, constituting, this way, a brand new method to control and adjust the diversity of the population. This detector has an extremely low computational load and offers an interesting alternative to previous suboptimal algorithms whose performance is frequently subject to the near-far problem and multiple access interference degradations. Its performance is compared with that of standard GA-based detectors, as well as traditional multiuser detectors, such as the matched filter, the decorrelator and the MMSE detectors.